For example, as a method of mechanically estimating an age of human using an identification device or the like on the basis of human facial image data, for example, there is a method of identifying an age itself, which is the method of estimating an age as a continuous quantity (the method in which age estimation is performed as a regression problem). Such a method is disclosed in Non-Patent Document 1, for example. Since continuous ages can be expressed, it has been demonstrated by experiments that the contradiction less arise as compared to the method in which age estimation is performed as an identification problem, and an age can be recognized with high accuracy.
The regression problem can be obtained by solving the difference between an estimated age (an age which is estimated) and a correct age (an age which is correct) as a problem of minimization. Specific examples include the multiple linear regression analysis and the (kernel) ridge regression. These methods execute learning so as to reduce the mean square error or the mean absolute error between the estimated age and the correct age.
FIG. 5 shows an example of the age estimation apparatus that performs age estimation as a regression problem. Generally, the image data to be inputted (for example, facial image data) is high dimensional data such as the number of pixels or the number of pixels×3 (color values of R, G, and B). Therefore, in a dimension compressor 61, features are extracted from image data such that age information is emphasized and unnecessary information (lighting condition, facial angle, and the like) is deleted. Thereby, the image data is converted into low dimensional data from high dimensional data. Here, in the dimension compressor 61, for example, methods such as the principal component analysis (PCA), the linear discriminant analysis (LDA), and the locality preserving projection (LPP) are employed. This processing is also referred to as the “feature selection” and “dimension compression”. Then, an identification device 62 estimates an age on the basis of the features extracted.
In order to estimate an age with an age estimation apparatus 60 on the basis of the image data, learning of the dimension compressor 61 and the identification device 62 is required. That is, plural image data of people whose correct ages (actual ages or perceptual ages (i.e., apparent age)) are known are inputted to the dimension compressor 61, and each data is evaluated by the methods such as the N-fold cross validation and the leave-one-out cross-validation. On the basis of this evaluation result, an output of the identification device 62 is adjusted so that the error (the difference between the estimated age and the correct age) would be reduced. For the learning of the identification device 62, the methods such as the linear regression, the multiple regression, the ridge regression, and the neural network are applied.
By repeating similar procedures while changing the type and combination of features, the extraction method (i.e., a parameter used for dimension compression), and the like, a parameter and a model are selected so that the error would be reduced.